Recommendation systems have been broadly used in various e-commerce applications such as targeted marketing, advertisement, personalized search, etc. Despite the widespread application of recommendation systems, capturing and adapting to users' change of interest are still an open problem for many domains and applications. Ignoring these changes may result in recommending items that are not interesting to the user anymore while they would match the user's previous interests. To solve this problem, one solution would be to limit a size of the profile to a maximum threshold or limit a user's profile to contain only preferences gathered in a recent fixed sized window of time. However, it is difficult if not impossible to find a fixed global threshold that would be optimal for all the users. Therefore, limiting the profile size would result in losing information about a user's preferences and making the recommendation systems less useful.